Serum Biomarkers of Iron Stores Are Associated with Increased Risk of All-Cause Mortality and Cardiovascular Events in Nondialysis CKD Patients, with or without Anemia
Bibliographic record
Abstract
Significance Statement Management of iron deficiency in patients with nondialysis CKD focuses on improving erythropoiesis. Studies in patients with heart failure with similar iron deficiency pathogenesis found that treating iron deficiency improves cardiovascular outcomes, regardless of anemia. To evaluate a possible anemia-independent association of iron stores with outcomes in individuals with nondialysis CKD, the authors studied patients in nephrology-based clinics from a multinational cohort. They show that iron deficiency, as reflected by transferrin saturation index, is associated with higher risk of mortality and cardiovascular events in patients with CKD, with or without anemia. Intervention studies addressing the effects of treating iron deficiency beyond effects on erythropoiesis are necessary to challenge the current anemia-focused paradigm of iron deficiency management in nondialysis CKD, and potentially foster better strategies for improving patient outcomes. Background Approximately 30%–45% of patients with nondialysis CKD have iron deficiency. Iron therapy in CKD has focused primarily on supporting erythropoiesis. In patients with or without anemia, there has not been a comprehensive approach to estimating the association between serum biomarkers of iron stores, and mortality and cardiovascular event risks. Methods The study included 5145 patients from Brazil, France, the United States, and Germany enrolled in the Chronic Kidney Disease Outcomes and Practice Patterns Study, with first available transferrin saturation (TSAT) and ferritin levels as exposure variables. We used Cox models to estimate hazard ratios (HRs) for all-cause mortality and major adverse cardiovascular events (MACE), with progressive adjustment for potentially confounding variables. We also used linear spline models to further evaluate functional forms of the exposure-outcome associations. Results Compared with patients with a TSAT of 26%–35%, those with a TSAT ≤15% had the highest adjusted risks for all-cause mortality and MACE. Spline analysis found the lowest risk at TSAT 40% for all-cause mortality and MACE. Risk of all-cause mortality, but not MACE, was also elevated at TSAT ≥46%. Effect estimates were similar after adjustment for hemoglobin. For ferritin, no directional associations were apparent, except for elevated all-cause mortality at ferritin ≥300 ng/ml. Conclusions Iron deficiency, as captured by TSAT, is associated with higher risk of all-cause mortality and MACE in patients with nondialysis CKD, with or without anemia. Interventional studies evaluating the effect on clinical outcomes of iron supplementation and therapies for alternative targets are needed to better inform strategies for administering exogenous iron.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".